# How to Configure MiniMind to Use YaRN for Longer Context Windows

> Configure MiniMind to use YaRN for longer contexts by setting inference_rope_scaling to True or passing the --inference_rope_scaling flag. Extend context beyond 32768 tokens.

- Repository: [jingyaogong/minimind](https://github.com/jingyaogong/minimind)
- Tags: how-to-guide
- Published: 2026-03-24

---

**Enable YaRN in MiniMind by passing the `--inference_rope_scaling` flag or setting `inference_rope_scaling=True` in `MiniMindConfig` to extrapolate rotary positional embeddings and extend the usable context beyond the default 32,768 tokens.**

MiniMind is a lightweight open-source language model that implements YaRN (Yet Another RoPE N-scaling) to handle longer input sequences without retraining or modifying model weights. Configuring MiniMind to use YaRN for longer contexts requires toggling a specific configuration flag that adjusts the rotary frequency computation in the model's attention mechanism.

## Understanding YaRN in MiniMind

YaRN modifies how **rotary positional embeddings (RoPE)** calculate frequencies to support sequences longer than the training context. When enabled, MiniMind applies a scaling factor to the inverse frequency calculation, allowing the model to generalize to extended contexts.

According to the source code in [`model/model_minimind.py`](https://github.com/jingyaogong/minimind/blob/main/model/model_minimind.py), setting `inference_rope_scaling=True` creates a `rope_scaling` dictionary with `"type": "yarn"` and a default **factor of 16** (providing 4× extrapolation capability) in the `MiniMindConfig` class (lines 56-65).

## Command-Line Configuration

The fastest way to activate YaRN is through MiniMind's CLI tools.

### OpenAI-Compatible API Server

When launching the FastAPI inference server via [`scripts/serve_openai_api.py`](https://github.com/jingyaogong/minimind/blob/main/scripts/serve_openai_api.py), append the `--inference_rope_scaling` argument:

```bash
python scripts/serve_openai_api.py \
    --load_from ../model \
    --weight full_sft \
    --hidden_size 512 \
    --max_seq_len 8192 \
    --inference_rope_scaling

```

The argument parser defines this flag at lines 70-73 of [`scripts/serve_openai_api.py`](https://github.com/jingyaogong/minimind/blob/main/scripts/serve_openai_api.py), injecting the YaRN configuration into the model initializer.

### Evaluation Scripts

For running benchmarks with extended contexts in [`eval_llm.py`](https://github.com/jingyaogong/minimind/blob/main/eval_llm.py):

```bash
python eval_llm.py \
    --model_path ../model \
    --max_seq_len 16384 \
    --inference_rope_scaling

```

This flag is handled at lines 37-42 of [`eval_llm.py`](https://github.com/jingyaogong/minimind/blob/main/eval_llm.py), enabling YaRN scaling during the evaluation phase without code modifications.

## Programmatic Python Configuration

For custom inference pipelines, instantiate `MiniMindConfig` with YaRN enabled:

```python
from model.model_minimind import MiniMindConfig, MiniMindModel

cfg = MiniMindConfig(
    hidden_size=512,
    num_hidden_layers=8,
    max_position_embeddings=32768,
    inference_rope_scaling=True  # Activate YaRN

)

model = MiniMindModel(cfg).to('cuda')

```

When `inference_rope_scaling=True`, the configuration object automatically populates the `rope_scaling` attribute with YaRN-specific parameters at lines 56-65 of [`model/model_minimind.py`](https://github.com/jingyaogong/minimind/blob/main/model/model_minimind.py).

### Customizing YaRN Parameters

To adjust the scaling behavior beyond the defaults, manually configure the `rope_scaling` dictionary after instantiation:

```python
cfg = MiniMindConfig(inference_rope_scaling=True)
cfg.rope_scaling = {
    "type": "yarn",
    "factor": 32,        # 8× extrapolation

    "beta_fast": 64,
    "beta_slow": 2,
    "original_max_position_embeddings": 2048,
    "attention_factor": 1.0
}

```

These parameters control the frequency adjustment in `precompute_freqs_cis` at lines 117-124 of [`model/model_minimind.py`](https://github.com/jingyaogong/minimind/blob/main/model/model_minimind.py), where the YaRN formula `f'(i) = f(i)·((1‑γ) + γ/s)` is applied to rescale rotary frequencies.

## Technical Implementation Details

The YaRN implementation resides in the `precompute_freqs_cis` function within [`model/model_minimind.py`](https://github.com/jingyaogong/minimind/blob/main/model/model_minimind.py). When `rope_scaling` is present, the code computes a linear ramp (γ) and modifies base frequencies according to the YaRN formula at lines 117-124. This rescaling allows MiniMind to maintain attention accuracy across sequences longer than the original training context.

Unlike truncation-based approaches, YaRN rescales rotary frequencies rather than limiting sequence length, preserving relative positional information for extended contexts while keeping the model weights frozen.

## Summary

- **Activate YaRN** using `--inference_rope_scaling` in CLI scripts or `inference_rope_scaling=True` in Python configs
- **Default scaling** provides 4× extrapolation with a factor of 16 (configured in `MiniMindConfig` at lines 56-65)
- **Implementation** modifies frequency computation in `precompute_freqs_cis` using the YaRN formula (lines 117-124 of [`model/model_minimind.py`](https://github.com/jingyaogong/minimind/blob/main/model/model_minimind.py))
- **Compatibility** works with [`serve_openai_api.py`](https://github.com/jingyaogong/minimind/blob/main/serve_openai_api.py), [`eval_llm.py`](https://github.com/jingyaogong/minimind/blob/main/eval_llm.py), and custom inference code without changing model weights

## Frequently Asked Questions

### What is the default YaRN scaling factor in MiniMind?

MiniMind defaults to a **factor of 16** when YaRN is enabled, which typically supports 4× extrapolation beyond the base context length. This value is automatically set in the `rope_scaling` dictionary when `inference_rope_scaling=True` is specified in the configuration.

### Can I use YaRN during training, or only for inference?

The configuration parameter is named `inference_rope_scaling`, indicating it is optimized for inference-time context extension. While the underlying RoPE scaling mechanism could theoretically apply to training, MiniMind's training scripts in the `trainer/` directory do not expose this parameter, suggesting YaRN is primarily intended for extending context during inference.

### How does YaRN differ from standard RoPE scaling methods?

YaRN (Yet Another RoPE N-scaling) calculates a linear interpolation factor (γ) and applies the formula `f'(i) = f(i)·((1‑γ) + γ/s)` to adjust frequencies, whereas standard linear scaling simply divides position indices by a constant factor. YaRN maintains better relative positional accuracy for longer contexts by specifically tuning the attention scale factors and frequency ramp parameters.

### What is the maximum effective context length with YaRN enabled?

While MiniMind's default `max_position_embeddings` is **32,768 tokens**, YaRN enables extrapolation significantly beyond this limit. With the default factor of 16, you can effectively process sequences up to approximately 131,072 tokens, though actual limits depend on available GPU memory and the specific scaling factor configured in the `rope_scaling` parameters.